A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (2024)

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A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (2)

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  • Pengcheng Zhou Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (3)0009-0001-4532-5680

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  • Lihua Zheng Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (4)0000-0002-0089-9437

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  • Minjuan Wang Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (5)0000-0002-7520-1726

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  • Minzan Li Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    Key Lab of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, China

    A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (6)0000-0002-4251-0020

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CVDL '24: Proceedings of the International Conference on Computer Vision and Deep LearningJanuary 2024Article No.: 41Pages 1–7https://doi.org/10.1145/3653781.3653825

Published:01 June 2024Publication HistoryA Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (7)

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CVDL '24: Proceedings of the International Conference on Computer Vision and Deep Learning

A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg

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A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (8)

ABSTRACT

Abstract: The application of automated apple harvesting technology significantly reduces labor costs and enhances picking efficiency. Currently, apple instance segmentation based on neural networks become popular approach because it offers higher accuracy compared to traditional apple detection models, and it can recognize apples at the pixel level. However, training these deep learning models means greater computational complexity and resource demands, which cannot be provided well in the apple picking field. To address these problems, we propose a lightweight algorithm model, YOLO-AppleSeg, built upon the YOLOv5s-seg model, specifically designed for instance segmentation of apple fruits in actual orchard environments, in which a novel lightweight convolution, EMSConv (Efficient Multi-Scale Convolution), is introduced. First, the image data from real-world scenarios consist of the following three parts: self-collected data, publicly accessible datasets, and images sourced from the Internet, and these data are labeled to construct a dataset for apple fruit instance segmentation modeling. Second, the YOLOv5s-seg model is improved to develop the lightweight YOLO-AppleSeg model for apple fruit instance segmentation. Finally, the model is trained, the ablation experiments are carried out, and model performance evaluation is conducted accordingly. The experimental results indicate that the proposed model achieves an AP50 score of 93.7 and an AP50-95 score of 74.6 for apple fruit instance segmentation, with only 30% of the parameters and 83% of the computational resources compared to the YOLOv5s-seg model, which is of significant importance for the automated harvesting of apple fruits in actual orchard field.

Keywords: Apple Detection; Automated Fruit Harvesting; Instance Segmentation; Convolution; Lightweight Model

References

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A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (43)

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      A Lightweight Apple Fruit Instance Segmentation Network: YOLO-AppleSeg | Proceedings of the International Conference on Computer Vision and Deep Learning (44)

      CVDL '24: Proceedings of the International Conference on Computer Vision and Deep Learning

      January 2024

      506 pages

      ISBN:9798400718199

      DOI:10.1145/3653804

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